Alexey Naumov

According to our database, Alexey Naumov authored at least 22 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

SCAFFLSA: Quantifying and Eliminating Heterogeneity Bias in Federated Linear Stochastic Approximation and Temporal Difference Learning.

Paul Mangold, Sergey Samsonov, Safwan Labbi, Ilya Levin, Réda Alami, Alexey Naumov, Eric Moulines

CoRR, 2024

2023

Simultaneous approximation of a smooth function and its derivatives by deep neural networks with piecewise-polynomial activations.

Denis Belomestny, Alexey Naumov, Nikita Puchkin, Sergey Samsonov

Neural Networks, April, 2023

Demonstration-Regularized RL.

Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard

CoRR, 2023

Finite-Sample Analysis of the Temporal Difference Learning.

Sergey Samsonov, Daniil Tiapkin, Alexey Naumov, Eric Moulines

CoRR, 2023

Generative Flow Networks as Entropy-Regularized RL.

Daniil Tiapkin, Nikita Morozov, Alexey Naumov, Dmitry P. Vetrov

CoRR, 2023

Theoretical guarantees for neural control variates in MCMC.

Denis Belomestny, Artur Goldman, Alexey Naumov, Sergey Samsonov

CoRR, 2023

Model-free Posterior Sampling via Learning Rate Randomization.

Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

First Order Methods with Markovian Noise: from Acceleration to Variational Inequalities.

Aleksandr Beznosikov, Sergey Samsonov, Marina Sheshukova, Alexander V. Gasnikov, Alexey Naumov, Eric Moulines

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Fast Rates for Maximum Entropy Exploration.

Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Yunhao Tang, Michal Valko, Pierre Ménard

Proceedings of the International Conference on Machine Learning, 2023

2022

Finite-time High-probability Bounds for Polyak-Ruppert Averaged Iterates of Linear Stochastic Approximation.

Alain Durmus, Eric Moulines, Alexey Naumov, Sergey Samsonov

CoRR, 2022

Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees.

Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Mark Rowland, Michal Valko, Pierre Ménard

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Local-Global MCMC kernels: the best of both worlds.

Sergey Samsonov, Evgeny Lagutin, Marylou Gabrié, Alain Durmus, Alexey Naumov, Eric Moulines

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

From Dirichlet to Rubin: Optimistic Exploration in RL without Bonuses.

Daniil Tiapkin, Denis Belomestny, Eric Moulines, Alexey Naumov, Sergey Samsonov, Yunhao Tang, Michal Valko, Pierre Ménard

Proceedings of the International Conference on Machine Learning, 2022

2021

Variance Reduction for Dependent Sequences with Applications to Stochastic Gradient MCMC.

Denis Belomestny, Leonid Iosipoi, Eric Moulines, Alexey Naumov, Sergey Samsonov

SIAM/ASA J. Uncertain. Quantification, 2021

Ex²MCMC: Sampling through Exploration Exploitation.

Evgeny Lagutin, Daniil Selikhanovych, Achille Thin, Sergey Samsonov, Alexey Naumov, Denis Belomestny, Maxim Panov, Eric Moulines

CoRR, 2021

Model-free policy evaluation in Reinforcement Learning via upper solutions.

Denis Belomestny, Ilya Levin, Eric Moulines, Alexey Naumov, Sergey Samsonov, Veronika Zorina

CoRR, 2021

Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize.

Alain Durmus, Eric Moulines, Alexey Naumov, Sergey Samsonov, Kevin Scaman, Hoi-To Wai

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

On the Stability of Random Matrix Product with Markovian Noise: Application to Linear Stochastic Approximation and TD Learning.

Alain Durmus, Eric Moulines, Alexey Naumov, Sergey Samsonov, Hoi-To Wai

Proceedings of the Conference on Learning Theory, 2021

2020

Variance reduction for Markov chains with application to MCMC.

Denis Belomestny, Leonid Iosipoi, Eric Moulines, Alexey Naumov, Sergey Samsonov

Stat. Comput., 2020

Finite Time Analysis of Linear Two-timescale Stochastic Approximation with Markovian Noise.

Maxim Kaledin, Eric Moulines, Alexey Naumov, Vladislav Tadic, Hoi-To Wai

Proceedings of the Conference on Learning Theory, 2020

2017

Improving the discoverability, accessibility, and citability of omics datasets: a case report.

Yolanda F. Darlington, Alexey Naumov, Apollo McOwiti, Wasula H. Kankanamge, Lauren B. Becnel, Neil J. McKenna

J. Am. Medical Informatics Assoc., 2017

A FAIR-Based Approach to Enhancing the Discovery and Re-Use of Transcriptomic Data Assets for Nuclear Receptor Signaling Pathways.

Scott A. Ochsner, Yolanda F. Darlington, Apollo McOwiti, Wasula H. Kankanamge, Alexey Naumov, Lauren B. Becnel, Neil J. McKenna

Data Sci. J., 2017